def rhyper(m, n, M, N, report=0):
'''
calculates cumulative probability based on
hypergeometric distribution
over/under/both (report = 0/1/2)
(uses R through RPy)
N = total balls in urn
M = total white balls in urn
n = drawn balls from urn
m = drawn white balls from urn
'''
from rpy import r
assert ((type(m) == type(n) == type(M) == type(N) == int) and m <= n
and m <= M and n <= N)
if report == 0:
#p-val for over-repr.
return r.phyper(m - 1, M, N - M, n, lower_tail=False)
elif report == 1:
#p-val for under-repr.
return r.phyper(m, M, N - M, n)
elif report == 2:
#tuple (over, under)
return r.phyper(m - 1, M, N - M, n,
lower_tail=False), r.phyper(m, M, N - M, n)
else:
raise "unknown option"
def rhyper(m, n, M, N, report=0):
'''
calculates cumulative probability based on
hypergeometric distribution
over/under/both (report = 0/1/2)
(uses R through RPy)
N = total balls in urn
M = total white balls in urn
n = drawn balls from urn
m = drawn white balls from urn
'''
from rpy import r
assert( (type(m) == type(n) == type(M) == type(N) == int)
and m <= n and m <= M and n <= N)
if report == 0:
#p-val for over-repr.
return r.phyper(m-1, M, N-M, n, lower_tail=False)
elif report == 1:
#p-val for under-repr.
return r.phyper(m, M, N-M, n)
elif report == 2:
#tuple (over, under)
return r.phyper(m-1, M, N-M, n, lower_tail=False), r.phyper(m, M, N-M, n)
else:
raise "unknown option"
def _cluster_stat(self, mcl_id, vertex_set):
if vertex_set in self.cluster_memory:
entry = self.cluster_memory[vertex_set]
p_value_min = entry[0]
go_no_vector = entry[1]
unknown_gene_ratio = entry[2]
self.to_db.append([p_value_min, go_no_vector, unknown_gene_ratio, mcl_id])
self.no_of_records += 1
return
else:
_cluster_memroy = {}
vertex_list = vertex_set[1:-1].split(',')
vertex_list = map(int, vertex_list)
cluster_size = len(vertex_list)
self.local_go_no_dict_construct(vertex_list)
if 0 in self.local_go_no_dict:
unknown_gene_ratio = self.local_go_no_dict[0]/float(cluster_size)
else:
unknown_gene_ratio = 0
if self.local_go_no_dict == {}:
self.logfile.write('%d %s: local_go_no_dict empty\n'%(mcl_id, repr(vertex_set)))
return
for go_no in self.local_go_no_dict:
if self.wu:
# code after 'or' deals with the situation that Jasmine's strategy is applied to whole gene-set(unknown included)
x = self.local_go_no_dict[go_no]
m = self.global_go_no_to_size_dict[go_no]
n = self.no_of_genes - m
k = cluster_size
else:
pass
if self.bonferroni:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)*len(self.local_go_no_dict)
else:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)
self.logfile.write('%d %d %d %d %d %d %f %f\n'%(mcl_id,go_no,x,m,n,k,p_value, unknown_gene_ratio))
if p_value in _cluster_memroy:
_cluster_memroy[p_value].append(go_no)
else:
_cluster_memroy[p_value] = [go_no]
p_value_vector = _cluster_memroy.keys()
if p_value_vector == []:
self.logfile.write('%d %s: all vertices belong to population singleton classes\n'%(mcl_id, repr(vertex_set)))
return
p_value_min = min(p_value_vector)
go_no_vector = _cluster_memroy[p_value_min]
self.no_of_records += 1
self.cluster_memory[vertex_set] =[p_value_min, go_no_vector, unknown_gene_ratio]
self.to_db.append([p_value_min, go_no_vector, unknown_gene_ratio, mcl_id])
def _cluster_stat(self, mcl_id, vertex_set, connectivity):
vertex_list_all = vertex_set[1:-1].split(',')
vertex_list = []
for i in range(len(vertex_list_all)):
vertex_list_all[i] = int(vertex_list_all[i])
if vertex_list_all[i] in self.global_gene_to_go_dict:
#this filter will only be useful when Jasmine's strategy is applied to whole gene-set(unknown included)
vertex_list.append(vertex_list_all[i])
cluster_size = len(vertex_list)
p_value_vector = [1] * self.no_of_functions
self.local_go_no_dict_construct(vertex_list)
for gene_no in vertex_list_all:
self.go_no_dict_adjust(gene_no)
for go_no in self._local_go_no_dict:
if self.wu or (gene_no not in self.global_gene_to_go_dict):
# code after 'or' deals with the situation that Jasmine's strategy is applied to whole gene-set(unknown included)
x = self._local_go_no_dict[go_no]
m = self._global_go_no_dict[go_no]
n = self.no_of_genes - m
k = cluster_size
else:
x = self._local_go_no_dict[go_no]
m = self._global_go_no_dict[go_no]
n = self.no_of_genes -1 - m
k = cluster_size-1
if self.bonferroni:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)*len(self._local_go_no_dict)
else:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)
if self.log:
self.logfile.write('%d %d %d %d %d %d %d %f\n'%\
(mcl_id,gene_no,go_no,x,m,n,k,p_value))
p_value_vector[go_no] = p_value
#for the unknown class, use the ratio instead of p_value, in accordance with mcl_result_stat.py
if self.wu:
p_value_vector[0] = self._local_go_no_dict[0]/float(cluster_size)
else:
#not wu's strategy, throw away the gene, the cluster_size is down by 1.
if self._local_go_no_dict.has_key(0):
#after leave_one_out, still unknown genes present
p_value_vector[0] = self._local_go_no_dict[0]/float(cluster_size-1)
else:
#no unknown genes
p_value_vector[0] = 1
if self.output:
self.outf.write('%d\t%d\t%s\t%f\n'%(mcl_id, gene_no, repr(p_value_vector), connectivity))
elif self.needcommit:
self.curs.execute("insert into %s(mcl_id, leave_one_out, p_value_vector, connectivity)\
values(%d, %d, ARRAY%s, %8.6f)"%(self.target_table, mcl_id, gene_no, repr(p_value_vector), connectivity))
self.no_of_records += 1
def get_information_of_go_functions(self, curs, go_no2association_genes, cluster_size, \
no_of_total_genes, p_value_cut_off=0, go_table='go'):
"""
04-06-05
input: go_no_list
output: go_no2information
information includes go_no, go_id, name, depth, no_of_associated genes
"""
sys.stderr.write("Getting information about a list of go_nos...")
go_no2information = {}
for go_no,association_genes in go_no2association_genes.iteritems():
no_of_associated_genes = len(association_genes)
curs.execute("select go_no, go_id, name, depth, array_upper(gene_array,1) from %s \
where go_no=%s"%(go_table, go_no))
rows = curs.fetchall()
for row in rows:
p_value = r.phyper(no_of_associated_genes-1, row[-1],no_of_total_genes-row[-1], cluster_size,lower_tail = r.FALSE)
if p_value_cut_off: #non zero, needs cut some p-values
if p_value>p_value_cut_off:
continue
go_no2information[go_no] = list(row) + [no_of_associated_genes, p_value] #go_no, go_id, name, depth, population size, local size, p_value
sys.stderr.write("Done.\n")
return go_no2information
def _cluster_stat(self, mcl_id, vertex_set):
vertex_list = vertex_set[1:-1].split(',')
vertex_list = map(int, vertex_list)
vertex_list_gene_symbol = []
for vertex in vertex_list:
vertex_list_gene_symbol.append(self.gene_no2gene_id[vertex])
cluster_size = len(vertex_list)
local_go_no_dict = self.local_go_no_dict_construct(vertex_list)
if local_go_no_dict == {}:
self.logfile.write('%d %s: local_go_no_dict empty\n'%(mcl_id, repr(vertex_set)))
return
for go_no in local_go_no_dict:
if self.wu:
# code after 'or' deals with the situation that Jasmine's strategy is applied to whole gene-set(unknown included)
x = len(local_go_no_dict[go_no])
m = self.global_go_no_to_size_dict[go_no]
n = self.no_of_genes - m
k = cluster_size
else:
pass
if self.bonferroni:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)*len(local_go_no_dict)
else:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)
if x >1:
#this function must have more than one gene associated.
transfac_dict = self.transfac_dict_construct(local_go_no_dict[go_no])
gene_id_list = []
for gene_no in local_go_no_dict[go_no]:
gene_id_list.append(self.gene_no2gene_id[gene_no])
self.logfile.write('%d\t%s\t%d\t%d\t%d\t%d\t%f\t%s\t%s\t%s\n'%(mcl_id,\
self.global_go_no2go_name[go_no], x, k, m, self.no_of_genes,\
p_value, '|'.join(gene_id_list), repr(vertex_list_gene_symbol), repr(transfac_dict)))
self.no_of_records += 1
def _cluster_stat(self, mcl_id, vertex_set, connectivity):
"""
04-18-05
add two important criteria to avoid the situation that hypergeometric test
is powerless (the population size of the go-no is too small).
1. percentage of associated-genes over total known genes >= uniformity (0.5 default)
2. apart from the percentage, the absolute number is also needed in case the cluster is too small.
04-19-05
fix a bug. self._no_of_known_genes_of_the_cluster could be 0.
"""
vertex_list_all = vertex_set[1:-1].split(',')
vertex_list = []
for i in range(len(vertex_list_all)):
vertex_list_all[i] = int(vertex_list_all[i])
if vertex_list_all[i] in self.global_gene_to_go_dict:
#this filter will only be useful when Jasmine's strategy is applied to whole gene-set(unknown included)
vertex_list.append(vertex_list_all[i])
cluster_size = len(vertex_list)
p_value_vector = [1] * self.no_of_functions
self.local_go_no_dict_construct(vertex_list)
for gene_no in vertex_list_all:
self.go_no_dict_adjust(gene_no)
for go_no in self._local_go_no_dict:
if self.global_go_no_to_size_dict[go_no]<self.min_node_size: #06-11-05
continue
if self.go_no2depth[go_no]>self.max_node_depth: #06-11-05
continue
if self.wu or (gene_no not in self.global_gene_to_go_dict):
# code after 'or' deals with the situation that Jasmine's strategy is applied to whole gene-set(unknown included)
x = self._local_go_no_dict[go_no]
m = self._global_go_no_dict[go_no]
n = self.no_of_genes - m
k = cluster_size
else:
x = self._local_go_no_dict[go_no]
m = self._global_go_no_dict[go_no]
n = self.no_of_genes -1 - m
k = cluster_size-1
if self._no_of_known_genes_of_the_cluster == 0:
go_no_ratio = 0
else:
go_no_ratio = float(x)/self._no_of_known_genes_of_the_cluster #NOTE: it's different from no_of_known_genes_of_the_cluster
if go_no_ratio < self.uniformity and go_no!=0: #It doesn't apply to the 0(unknown) category.
#ignore the function category if its percentage is < uniformity
continue
if x < 3 and go_no!=0: #apart from the percentage, the absolute number is also needed in case the cluster is too small.
continue
if self.bonferroni:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)*len(self._local_go_no_dict)
else:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)
if self.log:
self.logfile.write('%d %d %d %d %d %d %d %f\n'%\
(mcl_id,gene_no,go_no,x,m,n,k,p_value))
p_value_vector[go_no] = p_value
#for the unknown class, use the ratio instead of p_value, in accordance with mcl_result_stat.py
if self.wu:
p_value_vector[0] = self._local_go_no_dict[0]/float(cluster_size)
else:
#not wu's strategy, throw away the gene, the cluster_size is down by 1.
if self._local_go_no_dict.has_key(0):
#after leave_one_out, still unknown genes present
p_value_vector[0] = self._local_go_no_dict[0]/float(cluster_size-1)
else:
#no unknown genes
p_value_vector[0] = 1
#03-18-05increment before inserted into table, cluster_stat_id starting from 1
self.no_of_records += 1
if self.output:
self.outf.write('%d\t%d\t%s\t%f\n'%(mcl_id, gene_no, repr(p_value_vector), connectivity))
elif self.needcommit:
self.curs.execute("insert into %s(cluster_stat_id, mcl_id, leave_one_out, p_value_vector, connectivity)\
values(%d, %d, %d, ARRAY%s, %8.6f)"%(self.target_table, self.no_of_records, mcl_id, gene_no, repr(p_value_vector), connectivity))
def _cluster_stat(self, mcl_id, vertex_set, connectivity):
"""
04-18-05
add two important criteria to avoid the situation that hypergeometric test
is powerless (the population size of the go-no is too small).
1. percentage of associated-genes over total known genes >= uniformity (0.5 default)
2. apart from the percentage, the absolute number is also needed in case the cluster is too small.
04-19-05
fix a bug. self._no_of_known_genes_of_the_cluster could be 0.
08-13-05
remove the redundant information in the p_value_vector to save space
p_value_vector only keeps (p_value,go_no) pairs with lowest p_value and go_no>=min_node_depth
unknown_gene_ratio is split from p_value_vector
a bug found, p_value_vector is not grounded after each gene cycle
table submit() is not supported anymore
08-15-05
if go_no == 0, skip p-value calculation
self.local_go_no_dict[go_no]<3, skip the go_no. previously it's using self._local_go_no_dict[go_no] to do filter,
known genes and unknown genes get different treatment.
"""
vertex_list_all = vertex_set[1:-1].split(',')
vertex_list = []
for i in range(len(vertex_list_all)):
vertex_list_all[i] = int(vertex_list_all[i])
if vertex_list_all[i] in self.global_gene_to_go_dict:
#this filter will only be useful when Jasmine's strategy is applied to whole gene-set(unknown included)
vertex_list.append(vertex_list_all[i])
cluster_size = len(vertex_list)
self.local_go_no_dict_construct(vertex_list)
for gene_no in vertex_list_all:
p_value_vector = []
self.go_no_dict_adjust(gene_no)
#for the unknown class, use the ratio instead of p_value, in accordance with mcl_result_stat.py
if self.wu:
unknown_gene_ratio = self._local_go_no_dict[0]/float(cluster_size) #08-13-05 unknown_gene_ratio is split from p_value_vector
else:
#not wu's strategy, throw away the gene, the cluster_size is down by 1.
if self._local_go_no_dict.has_key(0):
#after leave_one_out, still unknown genes present
unknown_gene_ratio = self._local_go_no_dict[0]/float(cluster_size-1)
else:
#no unknown genes
unknown_gene_ratio = 0
for go_no in self._local_go_no_dict:
if go_no == 0: #unknown function category doesn't need to do calculation
continue
if self.local_go_no_dict[go_no]<3: #apart from the percentage, the absolute number is also needed in case the cluster is too small.
#08-15-05 fix a bug here, previously it's using self._local_go_no_dict[go_no] to do filter. known genes and unknown genes get different treatment.
continue
if self.global_go_no_to_size_dict[go_no]<self.min_node_size: #06-11-05
continue
if self.go_no2depth[go_no]<self.min_node_depth: #06-11-05, 08-13-05 max_node_depth changed to min_node_depth
continue
if self.wu or (gene_no not in self.global_gene_to_go_dict):
# code after 'or' deals with the situation that Jasmine's strategy is applied to whole gene-set(unknown included)
x = self._local_go_no_dict[go_no]
m = self._global_go_no_dict[go_no]
n = self.no_of_genes - m
k = cluster_size
else:
x = self._local_go_no_dict[go_no]
m = self._global_go_no_dict[go_no]
n = self.no_of_genes -1 - m
k = cluster_size-1
if self._no_of_known_genes_of_the_cluster == 0:
go_no_ratio = 0
else:
go_no_ratio = float(x)/self._no_of_known_genes_of_the_cluster #NOTE: it's different from no_of_known_genes_of_the_cluster
if go_no_ratio < self.uniformity and go_no!=0: #It doesn't apply to the 0(unknown) category.
#ignore the function category if its percentage is < uniformity
continue
if self.bonferroni:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)*len(self._local_go_no_dict)
else:
p_value = r.phyper(x-1,m,n,k,lower_tail = r.FALSE)
if self.log:
self.logfile.write('%d %d %d %d %d %d %d %f\n'%\
(mcl_id,gene_no,go_no,x,m,n,k,p_value))
p_value_vector.append([p_value, go_no]) #08-13-05
if len(p_value_vector) == 0: #08-13-05 no qualified go_no for this gene_no
continue
p_value_vector = self.retain_min_p_value_pairs(p_value_vector) #08-13-05 get the pairs with minimum p_value
#03-18-05increment before inserted into table, cluster_stat_id starting from 1
self.no_of_records += 1
if self.output:
self.outf.write('%d\t%d\t%s\t%f\t%f\n'%(mcl_id, gene_no, repr(p_value_vector), connectivity, unknown_gene_ratio)) #08-13-05
elif self.needcommit:
self.curs.execute("insert into %s(cluster_stat_id, mcl_id, leave_one_out, p_value_vector, connectivity)\
values(%d, %d, %d, ARRAY%s, %8.6f)"%(self.target_table, self.no_of_records, mcl_id, gene_no, repr(p_value_vector), connectivity))
